Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach | Paper Citation Record

Observation 568f800f-14aa-4918-98fd-4b2d03a797b7 · outbound

This paper cites Pie: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Pie: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.639269Z

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Observation 5763cc60-e779-4e33-b290-31d4a514cefa · outbound

This paper cites Cou- pling intent and action for pedestrian crossing behavior prediction,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Cou- pling intent and action for pedestrian crossing behavior prediction,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.624792Z

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Observation f6e05f4a-60f8-4c73-8e65-1a4233f97304 · outbound

This paper cites The Cityscapes Dataset for Semantic Urban Scene Understanding,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach The Cityscapes Dataset for Semantic Urban Scene Understanding,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.610106Z

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Observation 67a158c8-3c86-42f0-a035-6ae7ca028433 · outbound

This paper cites BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.596405Z

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Observation 0c3aecce-d412-4c3a-abd8-bb1415242f46 · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Are we ready for autonomous driving? the kitti vision benchmark suite,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.582226Z

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Observation e7d93c29-bda3-47d4-8623-2df3681921ef · outbound

This paper cites Vulnerable road users and connected autonomous vehicles interaction: A survey,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Vulnerable road users and connected autonomous vehicles interaction: A survey,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.567220Z

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Observation b3c3c09f-c908-4a7a-b231-34f167fd709d · outbound

This paper cites Vulnerable Road Users, Position/Policy Statement, NATIONAL SAFETY COUNCIL,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Vulnerable Road Users, Position/Policy Statement, NATIONAL SAFETY COUNCIL,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.552933Z

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Observation 89530718-1beb-488d-8d21-32943396e354 · outbound

This paper cites VULNERABLE ROAD USER (VRU) PROTECTION,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach VULNERABLE ROAD USER (VRU) PROTECTION,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.538862Z

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Observation a40eba53-da14-4277-ab68-7ec3e8e8fe52 · outbound

This paper cites Spatiotemporal relationship reasoning for pedestrian intent prediction,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Spatiotemporal relationship reasoning for pedestrian intent prediction,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.524863Z

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Observation f9dd9b16-b485-4d70-beb1-8d9c7b237ebe · outbound

This paper cites IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T16:40:03.716516Z

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Observation dd0adcbd-ee5a-4aed-81a1-768f790586cd · outbound

This paper cites Fussi-net: Fusion of spatio-temporal skeletons for intention prediction network,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Fussi-net: Fusion of spatio-temporal skeletons for intention prediction network,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.510210Z

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Observation 9ec45aff-4c6d-4f2b-a8b5-14236fa212b2 · outbound

This paper cites Context model for pedestrian intention prediction using factored latent-dynamic condi- tional random fields,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Context model for pedestrian intention prediction using factored latent-dynamic condi- tional random fields,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.495361Z

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Observation 74ae767c-dad8-46c8-911f-d8f00f81e758 · outbound

This paper cites Real-time intent prediction of pedestrians for autonomous ground vehicles via spatio-temporal densenet,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Real-time intent prediction of pedestrians for autonomous ground vehicles via spatio-temporal densenet,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.480856Z

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Observation 8365f61a-bfd2-4c39-ac66-9499c797b60c · outbound

This paper cites Intent prediction of vulnerable road users from motion trajec- tories using stacked lstm network,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Intent prediction of vulnerable road users from motion trajec- tories using stacked lstm network,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.466155Z

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Observation 51a90498-86c8-4c07-857b-1e2893288e05 · outbound

This paper cites Behavioral reasoning theory: Identifying new linkages underlying intentions and behavior,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Behavioral reasoning theory: Identifying new linkages underlying intentions and behavior,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.450766Z

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Observation 4ae50168-bd59-4604-8b2a-583fd685b62b · outbound

This paper cites Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.434535Z

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Observation 90a4ec05-349a-4e27-99f1-89a6d010f039 · outbound

This paper cites Bifold and semantic reasoning for pedestrian behavior prediction,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Bifold and semantic reasoning for pedestrian behavior prediction,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.419488Z

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Observation e012cb25-675e-43f3-b0df-31c889fc1d0e · outbound

This paper cites Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T16:40:03.750274Z

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Observation d1522eee-9dc4-4fc8-8245-f8c82b8ab1a6 · outbound

This paper cites Graph-sim: A graph-based spatiotemporal interaction mod- elling for pedestrian action prediction,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Graph-sim: A graph-based spatiotemporal interaction mod- elling for pedestrian action prediction,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.404550Z

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Observation 0bc10c02-f63f-4a4e-b6f2-21d04127dc86 · outbound

This paper cites Benchmark for evaluating pedestrian action prediction,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Benchmark for evaluating pedestrian action prediction,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.389655Z

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Observation 176d0f86-1ff7-4fc7-a48a-b45081c16553 · outbound

This paper cites Social aware multi- modal pedestrian crossing behavior prediction,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Social aware multi- modal pedestrian crossing behavior prediction,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.375006Z

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Observation 79b3d094-d01e-4746-8fb6-d09c7010cd3b · outbound

This paper cites Multi-modal hybrid architecture for pedestrian action prediction,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Multi-modal hybrid architecture for pedestrian action prediction,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T16:40:03.769103Z

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Observation c3546144-e90a-4cd4-895d-f4d49cae9d6b · outbound

This paper cites Context-aware captions from context-agnostic supervision,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Context-aware captions from context-agnostic supervision,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.351676Z

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Observation dbb340db-5581-4307-bc4a-47099257c92a · outbound

This paper cites Grounding visual explanations,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Grounding visual explanations,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.336704Z

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Observation 06854ead-d220-4a54-b6f7-183f00965aa0 · outbound

This paper cites Show, attend and tell: Neural image caption generation with visual attention,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Show, attend and tell: Neural image caption generation with visual attention,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.321995Z

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Observation 73bac432-0cae-4bfe-95fd-29bd18519ba7 · outbound

This paper cites Deep learning,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Deep learning,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T16:40:03.786667Z

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Observation 0bc3f2bc-5344-468a-a50d-73b6530e7058 · outbound

This paper cites Textual explanations for self-driving vehicles,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Textual explanations for self-driving vehicles,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T16:40:03.790784Z

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Observation d28c0620-92c0-427f-9d2d-dc345db748b4 · outbound

This paper cites PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T16:40:03.794969Z

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Observation 25d4b08d-5d15-4fdf-89df-51030b0eb142 · outbound

This paper cites A peek into the reasoning of neural networks: Interpreting with structural visual concepts,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach A peek into the reasoning of neural networks: Interpreting with structural visual concepts,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.288849Z

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Observation dd806b61-2a96-47cc-af7b-d2e74a17fe46 · outbound

This paper cites Drive: Deep reinforced accident anticipation with visual explanation,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Drive: Deep reinforced accident anticipation with visual explanation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.274600Z

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Observation ba0c6d4d-9b07-4d7c-abe2-3179f0852e98 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Learning transferable visual models from natural language supervision,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.260548Z

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Observation 4abd2fe3-18e1-4546-8eb5-fc2cf823376a · outbound

This paper cites Multimodal contrastive training for visual representation learning,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Multimodal contrastive training for visual representation learning,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.245922Z

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Observation b808f374-c887-4a54-83be-9003ac329812 · outbound

This paper cites Shared cross-modal trajectory prediction for autonomous driving,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Shared cross-modal trajectory prediction for autonomous driving,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.231322Z

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Observation 15fd85e8-2b30-4975-a472-22a61e8b1bb5 · outbound

This paper cites Pedestrian inten- tion prediction for autonomous driving using a multiple stakeholder perspective model,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Pedestrian inten- tion prediction for autonomous driving using a multiple stakeholder perspective model,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.216666Z

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Observation 88216d5f-0ceb-4fa5-85d4-b3ceb6a868c6 · outbound

This paper cites Joint intention and trajectory prediction based on transformer,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Joint intention and trajectory prediction based on transformer,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.202042Z

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Observation e0e8b0f5-df33-4674-8124-51826c398201 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T16:40:03.829524Z

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Observation b91b304d-7b8f-4f03-82ad-8c02bde4ffc1 · outbound

This paper cites Learning phrase representations using rnn encoder-decoder for statistical machine translation,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Learning phrase representations using rnn encoder-decoder for statistical machine translation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.187401Z

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Observation 98a66487-ecdc-4f68-943c-20f90c88b21d · outbound

This paper cites Swin transformer v2: Scaling up capacity and resolution,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Swin transformer v2: Scaling up capacity and resolution,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.171750Z

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Observation 518a878e-8eba-461a-897e-fb2827469745 · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Sentence-bert: Sentence embeddings using siamese bert-networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.156832Z

Source-reported events for the cited work

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Observation 42dcefa3-7547-410f-83ca-73609671d087 · outbound

This paper cites Attention is all you need,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Attention is all you need,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.142526Z

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Observation 8b916601-dfe5-494d-aeb5-40b01c134e68 · outbound

This paper cites Pedx: Bench- mark dataset for metric 3-d pose estimation of pedestrians in complex urban intersections,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Pedx: Bench- mark dataset for metric 3-d pose estimation of pedestrians in complex urban intersections,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.128592Z

Source-reported events for the cited work

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Observation aebda185-ae3f-4736-81f3-cadb7a499fd6 · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach nuscenes: A multimodal dataset for autonomous driving,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.114367Z

Source-reported events for the cited work

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Observation 39df48ca-33c1-4177-aa32-124a29a055d3 · outbound

This paper cites Intraclass correlations: uses in assessing rater reliability.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Intraclass correlations: uses in assessing rater reliability

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T16:40:03.858818Z

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Observation c6733adf-19b0-4473-953b-00b6165d84f3 · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Semi-supervised classification with graph convolutional networks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.089604Z

Source-reported events for the cited work

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Observation efa28b45-4881-43da-8f93-176d882935b2 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Imagenet large scale visual recognition challenge,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.072861Z

Source-reported events for the cited work

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Observation 13f3b255-7bbf-4acc-8a76-aaa13f3d7bc7 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach An image is worth 16x16 words: Transformers for image recognition at scale,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.058470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5412dace-c436-4833-ad98-cf17642c8ea4 · outbound

This paper cites Learning deep transformer models for machine translation,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Learning deep transformer models for machine translation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.042105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 874c0def-2dca-4b90-a628-7da34f793aad · outbound

This paper cites Adaptive input representations for neural language modeling,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Adaptive input representations for neural language modeling,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.027120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b6fc43bc-29f3-4637-916d-3a4109968276 · outbound

This paper cites Effective approaches to attention-based neural machine translation,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Effective approaches to attention-based neural machine translation,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:40:04.011714Z

Source-reported events for the cited work

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Observation 414845fc-03fb-464f-aa81-25d71811f9c1 · outbound

This paper cites Pedestrian Intention Prediction: A Multi-task Perspective.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Pedestrian Intention Prediction: A Multi-task Perspective

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T16:40:03.888954Z

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Observation 1484627d-73cf-48fd-8fcd-f97a7dd25e0d · outbound

This paper cites Glove: Global vectors for word representation,.

Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Glove: Global vectors for word representation,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T16:40:03.893730Z

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